Development and Psychometric Properties of the Mental Health Literacy Scale for Women of Reproductive Age: A Mixed Method Study

ABSTRACT

Background and Aims

Gender is recognized as a critical determinant of mental health. Women's reproductive years are a period when they are particularly vulnerable to mental health issues. Enhancing mental health literacy can help reduce the burden of these disorders. This study aimed to develop and assess the psychometric properties of a Mental Health Literacy Scale specifically designed for women of reproductive age.

Methods

Women of reproductive ages mental health literacy scale (WoRA‐MHL) was developed through qualitative studies, a literature review, and systematic item generation. The scale underwent content and face validity assessments, followed by item analysis. Structural validity was examined by employing preliminary factor analysis (EFA) and confirmatory factor analysis (CFA). Reliability measures included Cronbach's alpha, split‐half testing, intraclass correlation coefficients (ICC), and metrics of absolute reliability.

Results

The final WoRA‐MHL instrument consists of 30 items categorized under four themes: “Accessing and Obtaining Mental Health Information,” “Understanding Mental Health Information,” “Maintaining Mental Health,” and “Adapting to the Challenges of Women's Lives.” Together, these four themes accounted for 54.42% of the total variance. CFA validated a satisfactory model fit for the scale. The reliability assessment indicated a Cronbach's alpha of 0.889, an overall ICC of 0.966, and a standard error of measurement of 4.68.

Conclusion

The study offered initial support for the construct validity and reliability of the WoRA‐MHL as a tool for assessing mental health literacy (MHL) in reproductive‐age women. It aims to identify gaps in knowledge, skills, and behaviors related to mental health and mental disorders, as well as to assist in planning promotional programs and evaluating the effectiveness of interventions. Future research is needed to further validate this measure and conduct multicultural comparisons of MHL across diverse populations worldwide.

1. Introduction

Between 1990 and 2021, there was a 67.58% rise in the global prevalence of depression among women of reproductive age. The incidence of depression increased by 71.44%, and the number of disability‐adjusted life years (DALYs) associated with depression went up by 69.08%. Particularly during the COVID‐19 pandemic, these figures saw a significant spike: prevalence rose by 17.86%, incidence by 24.51%, and DALYs by 20.80% from 2019 to 2021. The age group of 15 to 19, across all sociodemographic index (SDI) levels, faced the steepest climbs in these statistics [1]. These figures highlight the critical necessity for focused interventions and additional research to tackle the rising mental health concerns, especially among women of reproductive age, in line with the WHO's goals for 2030 [2]. Studies show that 31.03% of the population suffers from mental disorders, with women exhibiting a greater prevalence of symptoms compared to men [3, 4].

Health literacy has evolved from a narrow focus on functional skills such as reading and writing to a broader, multidimensional concept emphasizing the development of knowledge, attitudes, and abilities required individuals to manage their health actively [5]. Mental health literacy (MHL) is widely regarded as a specialized subset of health literacy [6]. The World Health Organization (WHO) identifies health literacy as a foundation for MHL, highlighting its potential to empower individuals in decision‐making and self‐care, ultimately leading to significant health improvements [7]. Literacy is a critical human resource, and assessing literacy levels across different linguistic and cultural populations is essential [8].

Jorm described MHL as involving several vital components, including the capacity to identify specific mental health conditions, proficiency in obtaining mental health information, comprehension of contributing risk factors and underlying causes, familiarity with self‐management approaches and available professional services, and attitudes that facilitate early recognition and appropriate help‐seeking actions [9].

The WHO has also emphasized the heightened importance of women's health; particularly given the sociocultural discrimination they often face [10]. Gender is a significant determinant of health, including mental health [10]. In line with the WHO's advocacy for promoting knowledge and awareness, collecting data on women's mental health knowledge is vital to informing effective policy‐making and interventions. The concept of MHL can serve as a valuable framework for this purpose.

Developed countries have seen modest improvements in MHL, the levels of MHL in many developing regions, including the Middle East, remain inadequate [11]. An investigation conducted in Tehran (2020) revealed that pregnant women had only an average level of MHL with respect to postpartum depression [12]. This area remains underexplored in Iran despite the critical importance of women's health literacy. Health literacy is vital for policymakers to improve healthcare delivery and outcomes, as it significantly influences individuals' health‐related decisions [13].

Mental illness represents an escalating public health challenge, and improving MHL is an effective strategy for mitigating the burden of mental health disorders. Adequate MHL supports help‐seeking behaviors, early diagnosis and treatment, stigma reduction, and increased public awareness [13, 14]. With mental health services now integrated into primary healthcare in Iran—where psychologists are available in urban health centers to deliver mental health services—evaluating women's MHL can help guide the design and implementation of targeted interventions to enhance health outcomes and promote effective help‐seeking behaviors.

The development of reliable tools to assess MHL is critical for identifying, managing, and preventing mental health disorders. These tools also provide insights into the current understanding of MHL, enabling the creation of effective interventions. Existing MHL instruments have been applied in various countries and address dimensions such as mental disorder prevention, early symptom recognition, self‐help strategies, knowledge of treatment options, and support of individuals with mental disorders [5, 15, 16]. Reliable MHL measurement instruments are crucial for designing impactful strategies to enhance mental health. Such instruments support researchers and policymakers in pinpointing knowledge deficits and formulating culturally sensitive interventions aligned with individual and community needs. Designing such instruments requires a well‐defined construct of MHL [17, 18].

Around one in five women experience common mental health problems such as depression and anxiety [19]. Women are more likely than men to face certain challenges that can impact their mental well‐being. For instance, many women serve as caregivers, which can lead to increased stress, anxiety, and feelings of isolation. Additionally, women are more likely to live in poverty, which, coupled with concerns about personal safety and a predominant focus on domestic responsibilities, can further contribute to social isolation. Furthermore, women are at a higher risk of experiencing physical and sexual abuse, which can have long‐lasting effects on their mental health. Sexual violence can also lead to conditions like posttraumatic stress disorder (PTSD) [20, 21, 22].

Life experiences and shifts in hormones frequently play a crucial role in shaping women's mental well‐being. Conditions such as anxiety and depression are more commonly found in women, with certain forms of depression impacting women in distinct ways. Some women might experience mental health disorder symptoms during times of hormonal changes, such as perinatal depression, premenstrual dysphoric disorder, and depression related to menopause [21, 22]. In light of this, it is important to take into account the different facets of mental health literacy among women of reproductive age when developing mental health initiatives. This approach can greatly enhance their overall quality of life.

On the other hand, literature reviews revealed a significant gap in the assessment of women's MHL. Existing instruments, such as the MHL Scale [23], the Mental Health Knowledge Questionnaire [24], and the Mental Health Knowledge Scale [25], primarily address public MHL and lack a specific focus on women. Therefore, this investigation sought to design and assess the psychometric attributes of a mental health literacy (MHL) instrument specifically developed for women of reproductive age (WoRA‐MHL).

2. Methods

2.1. Investigation Design

This investigation employed a two‐phase approach to design and validate an instrument for measuring mental health literacy in reproductive ages women. Validity was established through exploratory factor analysis (EFA), confirmatory factor analysis (CFA), content validity, and internal consistency assessments. Furthermore, split‐half reliability was analyzed to verify the scale's reliability. Details of these procedures are presented in subsequent sections. The current research was conducted in various community settings, including health centers, neighborhood centers, cultural centers, gyms, parks, mosque associations, educational institutions (universities, schools, and colleges), and other public places. A total of 553 participants were recruited from three provinces in Iran: Tehran, Qazvin, and Alborz.

The inclusion criteria for participation in the study were as follows: participants had to be between 15 and 49 years old, willing to participate, and able to read and write (completion of primary education). A history of mental health issues was not considered an exclusion criterion. To form the expert panel, selection criteria included proven expertise and training in the fields of mental health and women's health, as well as a minimum of 10 years of relevant work experience. The characteristics of the participants at each stage of the investigation are described in subsequent sections.

For qualitative sampling, a Purposeful sampling method with maximum diversity was employed. Qualitative data collection took place from August 5, 2022, to March 15, 2023. For the validation phase, data were collected using a cross‐sectional study and an available random sampling method in an environment consistent with the qualitative research setting, from July 5, 2023, to November 20, 2023.

2.2. Phase 1: Concept analysis

The first phase involved a comprehensive concept analysis consisting of five steps: item generation, content validity assessment, face validity evaluation, and item analysis. These steps were carried out during the initial development phase of the questionnaire to ensure that the instrument was comprehensive and contextually relevant.

2.3. Item Generation

A qualitative investigation was undertaken to examine the experiences of women of reproductive age and generate an initial pool of items for the MHL instrument. Purposeful sampling was used to achieve maximum variation in age, education, employment status, socioeconomic status, marital status, and number of children. The inclusion criteria required participants to be women of reproductive age willing to participate, fluent in Persian, and capable of effective communication. Individual, in depth, and semi‐structured interviews were conducted face‐to‐face in private settings chosen by the participants. Key informants, including psychologists, psychiatrists, counselors, and health promotion professionals, were also interviewed to provide additional insights. Sampling continued until data saturation was reached, ensuring that no new information emerged from subsequent interviews. In total, 20 participants were interviewed, including 14 women of reproductive age and 6 key informants (Table 1).

Table 1.

Characteristics of participants in qualitative study.

Women
No	Age	Education	Job	Marital status	Number of children	Interview duration
1	37	Bachelor of geography	Housewife	Married	1	48:17
2	30	Bachelor of geography	Employed	Single	0	31:12
3	33	Master of chemistry	Employed	Married	1	38:17
4	23	Accountant	Employed	Single	0	28:05
5	20	Computer undergraduate student	University student	Single	0	29:55
6	42	Food industry expert	Employed	Married	0	38:26
7	37	Master of literature	Housewife	Married	1	31:05
8	37	Master of literature	Housewife	Married	1	31:05
9	35	History expert	Employed	Married	2	29:55
10	43	Architectural expert	Housewife	Married	2	37:15
11	15	Ninth grade	Student	Single	0	22:15
12	49	Middle school	Housewife	Married	3	35:15
13	28	Diploma	Housewife	Married	0	32:15
14	18	Diploma	Housewife	Married	1	35:36
Key informants
No	Education		Workplace			Interview duration
15	Senior consulting expert		Qazvin mental health department			38:49
16	PhD in psychology		22 Bahman Qazvin educational and therapeutic center			35:29
17	Psychiatrist		Private clinic			32:19
18	Master degree in psychiatric nursing		22 Bahman Qazvin educational and therapeutic center			25:10
19	PhD in psychology		22 Bahman Qazvin educational and therapeutic center			42:27
20	Psychologist		Comprehensive health service center			38:49

The interviews were guided by open‐ended questions such as the following:

• “Please tell me what you do to maintain and improve your health, particularly your mental health?”

• “What type of information about mental health are you most interested in?”

• “How do you obtain this information?”

• “How do you verify the validity of this information?”

• “How has finding mental health information impacted your life?”

Probing questions were utilized to gain deeper insights into the interview participants' experiences. With their consent, all interviews were audio‐recorded. The interviews ranged from 22 to 48 min, with an average length of 34 min. The recordings were transcribed verbatim shortly after the interviews [26]. The data were analyzed via an inductive content analysis approach, generating an initial pool of 120 items. The research team carefully reviewed these items in multiple sessions. Duplicate, unrelated, and overlapping items were removed or merged, After several rounds of review by the research team, 74 duplicate, unrelated, and overlapping items were removed. resulting in a refined pool of 46 items. The finalized 46‐item questionnaire utilized a 5‐point Likert scale (never, rarely, sometimes, usually, always) to measure the respondents' abilities, ranging from the lowest to the highest level. In the subsequent step, the content validity, face validity, and item analysis of this initial 46‐item questionnaire were evaluated.

2.4. Content Validity

Both qualitative and quantitative approaches were utilized to evaluate the content validity of the questionnaire. In the qualitative phase, 13 health education, reproductive health, and psychology experts reviewed the instrument. They assessed each question's grammar, phrasing, item allocation, and scaling [27]. Modifications were made on the basis of their feedback to improve the clarity and relevance of the items. In the quantitative phase, 13 experts rated each item via a 3‐point Likert scale (1 = essential, 2 = valuable but not essential, 3 = not essential). The content validity ratio (CVR) was computed for each item, with those receiving a CVR of 0.54 or higher, according to Lawshe's Table [28], being included in the final version of scale [29].

2.5. Face Validity

Both qualitative and quantitative approaches were utilized to establish face validity. For qualitative face validity, 10 women of reproductive age were selected through convenience sampling. The participants assessed the questionnaire for ambiguity, relevance, and difficulty in employing open‐ended feedback. No items were removed during this stage. Each item's impact score (IS) was calculated for the quantitative assessment. A separate group of 10 women of reproductive age rated the importance of each item on a 5‐point Likert scale (very important, important, relatively important, slightly important, unimportant). The IS was determined by multiplying the percentage of participants who rated the item as “important” or “very important” (scores of 4 or 5) by the mean importance rating of the item. The formula used was as follows:

$$\text{Item}\text{IS}=\text{Frequency}(\%)\times \text{Importance}$$ (1)

Items with an IS of 1.5 or higher were deemed acceptable [30].

2.6. Item Analysis (Pilot Testing)

Item analysis was performed on a pilot sample to refine the questionnaire before its construct validity was evaluated. A total of 50 women of reproductive age were recruited through a conventional sampling method and completed the MHL questionnaire [31, 32]. Each item was evaluated on the basis of its mean, standard deviation, correlation with other items, and effect on the internal consistency of the overall questionnaire. Decisions to retain or remove items were guided by the corrected item‐total correlation (ITC) and the impact of item removal on Cronbach's alpha coefficient. Items with negative correlations or correlations less than 0.3 with the total score were excluded [33]. This rigorous process ensured that the final version of the questionnaire achieved strong reliability and internal consistency.

2.7. Phase 2: Assessment of Psychometric Properties of the Questionnaire

2.7.1. Design and Participants

The psychometric properties of WoRA‐MHL were assessed through a cross‐sectional investigation conducted in Tehran and Qazvin, Iran, in 2023. A convenience sample of women of reproductive age was recruited from public venues (e.g., neighborhood halls, stadiums, cultural centers, parks, and educational institutions) and healthcare centers. The inclusion criteria for the investigation included being 15–49 years old, being willing to participate, and having the ability to read and write (completion of primary education). A sample size of 200 participants was considered adequate for both EFA and CFA [34].

The sample's suitability for factor analysis was evaluated via the Kaiser‐Meyer‐Olkin (KMO) measure of sampling adequacy and Bartlett's test of sphericity [34]. The participants completed the WoRA‐MHL questionnaire under the principal investigator's (AS) supervision. Demographic information, such as age, education level, occupation, marital status, number of children, and history of mental health disorders, was also gathered. Before performing EFA, item statistics such as the mean, standard deviation, skewness, and kurtosis were examined to assess data normality. The corrected ITCs were calculated to evaluate the commonality of each item with others and to detect redundancy. Structural validity and reliability assessments were conducted to establish the instrument's psychometric properties. EFA was conducted to evaluate the scale's construct validity using the maximum likelihood method with varimax rotation, and the factor structure was analyzed through Kaiser normalization. CFA was subsequently performed to validate the latent constructs identified in the EFA [35]. Additionally, CFA was used to assess the model's goodness of fit, convergent validity, and discriminant validity, confirming the scale's structural robustness.

2.8. Statistical Analysis

The quantitative data obtained from the questionnaire were analyzed to assess content validity, construct validity, and reliability via IBM SPSS Statistics for Windows (version 24.0) and AMOS (version 24.0). Content validity was assessed through a combination of quantitative and qualitative approaches. For the quantitative evaluation, the item‐level content validity index (I‐CVI) was derived by dividing the number of experts who rated an item as essential (3 or 4 on a 4‐point relevance scale) by the total number of experts. Items with I‐CVI values of 0.80 or above were retained. The scale‐level content validity index (S‐CVI/Ave) was calculated as the mean of all the I‐CVI scores, with a cut‐off value of 0.90 or higher deemed acceptable [36]. To enhance the questionnaire, item analysis was conducted by calculating each item's mean, standard deviation, skewness, kurtosis, and ITC. Items with skewness or kurtosis values exceeding 2.0 or ITC values below 0.30 were excluded to strengthen internal consistency. After each item was removed, Cronbach's alpha was recalculated to ensure that the reliability of the overall scale remained intact. Additionally, descriptive statistics were utilized to summarize demographic characteristics and participants' measurement data [36].

Construct validity was evaluated through EFA and CFA. Before EFA, the KMO test and Bartlett's test of sphericity were used to confirm that the data were appropriate for factor analysis [37]. EFA was carried out using the maximum likelihood estimation method with varimax rotation. To determine the recommended number of factor dimensions, we conducted scree plot inspections and parallel analysis based on minimum rank factor analysis (PA‐MRFA). The PA‐MRFA method evaluates model fit by reporting the overall percentage of common variance explained. We retained factors with loadings of 0.40 or higher, ensuring that they accounted for a cumulative variance of at least 60% [38]. To validate the factor structure identified in the EFA, CFA was employed, and the model's goodness‐of‐fit was evaluated using a range of indices, including chi‐square (χ²) with p > 0.05, standardized the chi‐square (χ²/df ≤ 3.0), root mean square residual (RMR ≤ 0.05), root mean square error of approximation (RMSEA ≤ 0.10), goodness‐of‐fit index (GFI ≥ 0.90), normed fit index (NFI ≥ 0.80), Tucker‒Lewis index (TLI ≥ 0.80), and comparative fit index (CFI ≥ 0.80) [39, 40].

Convergent validity was evaluated by calculating construct reliability (CR) and average variance extracted (AVE), with acceptable thresholds defined as CR > 0.70 and AVE > 0.50. Reliability was assessed through Cronbach's alpha for internal consistency and split‐half reliability [40]. The overall scale and subscales were considered reliable if Cronbach's alpha was greater than 0.7 [41]. These detailed analyses demonstrated that WoRA‐MHL is a robust psychometric tool for assessing mental health literacy among women of reproductive age.

2.9. Measurement Errors

The standard error of measurement (SEM) is a critical indicator of a test's measurement accuracy and absolute reliability. This method accounts for the variation and differences that inevitably arise due to errors when repeated measurements. This investigation utilized three metrics to evaluate measurement error: the SEM, the minimal detectable change (MDC), and the minimal important change (MIC). The MDC denotes the smallest measurable change in the instrument's score that surpasses the margin of error, indicating a statistically significant difference. In contrast, the MIC is the smallest score change considered meaningful or clinically relevant by the respondent or researcher [42, 43].

2.10. Scoring

The finalized WoRA‐MHL consists of 30 items categorized into four domains. Responses are recorded on a 5‐point Likert scale ranging from 1 (“never”) to 5 (“always”). Five negatively worded items were reverse‐scored to maintain consistency in interpretation. Subscale scores were calculated by totaling the item scores within each domain; the overall questionnaire score was obtained by summing the scores of all the items. Higher scores indicate greater levels of MHL. The instrument's total possible score ranges from 30 to 150. The questionnaire is designed to be completed in approximately 10–15 min and can be administered as a self‐administered or interviewer‐assisted survey. For enhanced interpretability and comparison across subscales, raw scores were linearly transformed to a 0–100 scale employing the following formula:

$$Scoreinpercent=\frac{Therawscoreobtained-Minimumpossiblescore}{Maximumpossiblescore-Minimumpossiblescore}\times 100$$

The scoring manual for the WoRA‐MHL is provided as a supplement, offering detailed instructions for calculating both subscale and total scores.

2.11. Feasibility and Interpretability

The feasibility of WoRA‐MHL was evaluated on the basis of the time required to complete the questionnaire and the frequency of missing responses for each item. A shorter completion time and minimal missing data indicate the practicality and user‐friendliness of the instrument. Interpretability was assessed by examining the effects of the ceiling and floor. Ceiling and floor effects were assessed by calculating the percentage of respondents who scored at the highest or lowest levels of the scale. If more than 15% of participants obtained either the maximum or minimum scores, it indicated the presence of ceiling or floor effects, suggesting potential limitations in the questionnaire's ability to differentiate between varying levels of MHL [30, 44].

3. Results

3.1. Phase 1: Concept Analysis

A total of 120 items were initially generated from the qualitative investigation. After several rounds of review by the research team, duplicate, unrelated, and overlapping, including 74 items were removed, resulting in an initial instrument containing 46 items. Following content validity, face validity, and item analysis, 16 items were removed per the previously established criteria. Ultimately, the final version of WoRA‐MHL included 30 items, which advanced to the structural validity stage.

3.1.1. Content Validity

During the content validity evaluation, 15 items were excluded on the basis of expert feedback. The experts assessed each item's relevance, clarity, and simplicity via a 4‐point Likert scale. Items with a CVI of 0.79 or higher were retained [28, 45]. The CVI scores for the remaining items ranged from 0.84 to 1, indicating satisfactory content validity. Both the CVR and CVI were calculated concurrently. The modified kappa values for the items ranged from 0.77 to 1, with an average kappa of 0.92. The S‐CVI/Ave for the entire scale was computed as 0.92, confirming WoRA‐MHL's robust content validity [35].

3.1.2. Face Validity

The ISs of the items ranged from 1.65 to 4.7, indicating acceptable importance levels. At this stage, no items were removed from the questionnaire.

3.1.3. Item Analysis (Pilot Testing)

During item analysis, one item was removed because the corrected ITC was less than 0.3. The final 30‐item questionnaire was subsequently advanced to the psychometric evaluation stage. The Cronbach's alpha coefficient for the entire instrument was calculated as 0.928, demonstrating excellent internal consistency (Table 2).

Table 2.

Internal consistency of the mental health literacy tool for women of reproductive age.

Items	The score of the questionnaire if the item is deleted	Variance in case if item deletion	Correlation of the item with the total score of the remaining items	Cronbach's alpha value in case of item deletion
I can get mental health information from different sources such as virtual networks, therapists and radio and television.	139.200	96.531	0.518	0.926
I find it difficult to use mental health apps on mobile.	139.300	96.745	0.413	0.927
I need a lot of time or someone else's help to read mental health educational material.	139.240	93.492	0.771	0.923
I am unable to recognize abnormal mental health symptoms (such as depression and anxiety).	139.640	91.500	0.649	0.924
There are difficult words in the brochures of psychoactive drugs that I do not know the meaning.	139.860	90.164	0.680	0.924
I understand information obtained from various mental health sources.	139.40	91.696	0.827	0.922
I can assess the accuracy of mental health information.	139.520	90.949	0.652	0.924
I know mood disorders related to female hormonal disorders (around menstruation, pregnancy, postpartum, premenopausal).	139.480	90.255	0.737	0.923
I use mental health information in my life.	139.180	95.579	0.679	0.925
I use the mental health information to improve my mood and relationships.	139.360	94.602	0.570	0.925
I know the normal range of mood swings related to my hormones (around menstruation, pregnancy, postpartum) and recognize abnormal ones.	139.380	90.853	0.739	0.923
I have control over my behavior and emotions (anger, stress, anxiety).	139.460	95.723	0.436	0.927
I know and apply the methods of controlling mood disorders (such as depression and anxiety).	139.700	95.847	0.341	0.928
I know my interests and positive and negative characteristics well and I have accepted them.	139.180	96.314	0.575	0.926
I try not to be in situations that increase my stress and anxiety.	139.200	96.408	0.534	0.926
I can control mood and behavior changes caused by hormonal disturbances.	139.360	96.439	0.385	0.927
I know mental health medical emergencies and I know what to do in the event of a mental health medical emergency.	139.360	91.745	0.638	0.924
I know the common side effects of mental health drugs.	140.080	92.973	0.268	0.937
I fear being judged by others for mental health disorders.	139.400	91.020	0.656	0.924
I know ways to relax myself.	139.280	97.185	0.372	0.927
I participate in various friendly and social groups to improve my mental health.	139.420	93.800	0.588	0.925
I am careful about the limits of my social and friendly relationships.	139.240	93.900	0.659	0.924
I have easily coped with the circumstances of changing the new role (role of wife, role of mother) that happened to me.	139.480	94.214	0.508	0.926
I deal with mental health issues related to my fertility (postpartum depression, postabortion depression, menstrual depression, pregnancy depression) easily.	139.560	94.700	0.466	0.927
I easily follow the therapist's treatment and care instructions.	139.120	96.475	0.683	0.926
If there is a need to take medication related to mental health, I will complete the course of treatment as prescribed by the therapist.	139.160	96.056	0.648	0.925
I can make decisions about activities that are good for my mental health (such as exercise and nutrition).	139.220	96.216	0.475	0.926
I deal with problems rationally and can adapt to changing circumstances.	139.240	95.166	0.579	0.925
I can decide when to see a doctor and mental health therapist.	139.280	97.308	0.358	0.928
In case of problems in the family, I support the mental health of my family members.	139.220	96.828	0.458	0.927
I get help from my family when I need help with my mental health.	139.240	97.900	0.309	0.928

3.2. Phase 2: Psychometric Properties of the Questionnaire

3.2.1. Demographic Characteristics

A total of 400 women aged 15–49 years participated in the investigation and completed the WoRA‐MHL questionnaire. The participants had a mean age of 28.05 years (SD = 8.86). Among the respondents, 39% were employed, while 61% were housewives. With respect to educational attainment, 4% had completed primary education, 20.5% had secondary education, and 75% had a university degree. Additionally, 17 participants reported a history of mental health disorders. Detailed demographic information is provided in Table 3.

Table 3.

Demographic characteristics of reproductive‐aged women (N = 400).

	Number	Percentage
Age
15–19	60	15
20–24	118	29.5
25–29	70	17.5
30–34	50	12.5
35–39	46	11.5
40–44	36	9
45–49	20	5
Job
Employed	156	39
Housewife	244	61
Education
Primary education	16	4
Secondary education	82	20.5
College education	302	75.5
Marriage
Married	186	46.5
Single	206	51.5
Divorced/widow	8	2
Number of children
0	238	59.5
1	72	18
2	50	12.5
3	34	8.5
4	6	1.5
History of mental illness
Yes	17	4.25
No	366	95.75
Type of mental illness
Depression	11	5.5
Obsession	3	1.5
Panic	1	0.5
Anxiety	2	1

3.2.2. Structural Validity

The structural validity of WoRA‐MHL was assessed by employing EFA to explore the underlying dimensions of the instrument. Scree plots, parallel analyses, and eigenvalues were used to determine the number of extracted factors. The KMO measure of sampling adequacy was 0.86, confirming that the sample size was sufficient for factor analysis. Furthermore, Bartlett's test of sphericity was significant, indicating adequate inter‐item correlations to justify factor extraction (Table 4).

Table 4.

Keyser–Meyer–Elkin index and Bartlett's test results.

KMO index	0.865
Bartlett's sphericity test
Chi‐square approximation	3406.387
Degree of freedom	435
The significance level	p < 0.001

The scree plot (Figure 1) and maximum likelihood estimation (MLE) supported a 4‐factor solution with eigenvalues greater than 1. Together, these four factors explained 54.42% of the total variance in the data set (Figure 2). After applying varimax rotation, all the items demonstrated factor loadings above 0.4, with each factor comprising at least three items. Items with loadings on multiple factors were assigned to the factor with the highest loading, and no items were removed from the analysis. The extracted factors were conceptually labeled to capture the thematic content of the items, comprising four core dimensions: access and obtain mental health information (4 items), understanding mental health information (6 items), ability to maintain mental health (9 items), and adapting to the challenges of women's lives (11 items). The detailed factor loadings and results from the analysis are outlined in Table 5.

Figure 1.

Sand chart of factors identified by exploratory factor analysis.

Figure 2.

Scale development process.

Table 5.

Results obtained from exploratory factor analysis for WoRA‐MHL.

	F1	F2	F3	F4
I can get mental health information from different sources such as virtual networks, therapists and radio and television.	0.565	0.141	0.060	0.320
I find it difficult to use mental health apps on mobile.	0.784	0.321	0.052	0.073
I need a lot of time or someone else's help to read mental health educational material.	0.585	0.367	0.203	0.017
I am unable to recognize abnormal mental health symptoms (such as depression and anxiety).	0.470	0.317	0.226	0.191
There are difficult words in the brochures of psychoactive drugs that I do not know the meaning.	0.268	0.695	0.253	0.145
I understand information obtained from various mental health sources.	0.252	0.734	0.071	0.200
I can assess the accuracy of mental health information.	0.134	0.659	0.210	0.276
I know mood disorders related to female hormonal disorders (around menstruation, pregnancy, postpartum, premenopausal).	0.255	0.537	0.200	0.266
I use mental health information in my life.	0.181	0.533	0.372	0.304
I use the mental health information to improve my mood and relationships.	0.085	0.565	0.092	0.448
I know the normal range of mood swings related to my hormones (around menstruation, pregnancy, postpartum) and recognize abnormal ones.	0.395	0.126	0.443	0.105
I have control over my behavior and emotions (anger, stress, anxiety).	0.143	0.317	0.436	0.359
I know and apply the methods of controlling mood disorders (such as depression and anxiety).	0.123	0.201	0.679	0.302
I know my interests and positive and negative characteristics well and I have accepted them.	0.115	0.274	0.521	0.365
I try not to be in situations that increase my stress and anxiety.	0.112	0.087	0.614	0.242
I can control mood and behavior changes caused by hormonal disturbances.	0.005	0.228	0.773	0.113
I know mental health medical emergencies and I know what to do in the event of a mental health medical emergency.	0.133	0.195	0.675	0.207
I fear being judged by others for mental health disorders.	0.356	0.152	0.607	0.069
I know ways to relax myself.	0.233	0.301	0.516	0.338
I participate in various friendly and social groups to improve my mental health.	0.400	0.009	0.388	0.411
I am careful about the limits of my social and friendly relationships.	0.393	0.258	0.235	0.606
I have easily coped with the circumstances of changing the new role (role of wife, role of mother) that happened to me.	0.310	0.001	0.415	0.530
I deal with mental health issues related to my fertility (postpartum depression, postabortion depression, menstrual depression, pregnancy depression) easily.	0.011	0.231	0.212	0.410
I easily follow the therapist's treatment and care instructions.	0.386	0.053	0.320	0.494
If there is a need to take medication related to mental health, I will complete the course of treatment as prescribed by the therapist.	0.370	0.040	0.252	0.626
I can make decisions about activities that are good for my mental health (such as exercise and nutrition).	0.304	0.162	0.369	0.504
I deal with problems rationally and can adapt to changing circumstances.	0.024	0.380	0.386	0.587
I can decide when to see a doctor and mental health therapist.	0.183	0.336	0.326	0.530
In case of problems in the family, I support the mental health of my family members.	0.012	0.260	0.061	0.677
I get help from my family when I need help with my mental health.	0.203	0.150	0.203	0.796
Eigenvalue	4.383	4.953	6.546	38.539
Explained variance (%)	10.014	15.892	12.434	16.081
Cumulative variance (%)	10.014	22.448	38.340	54.421

Note: F1: Access and obtain mental health information, F2: Understanding mental health information, F3: Ability to maintain mental health, F4: Adapting to the challenges of women's lives.

3.2.3. CFA and Reliability

CFA was conducted to validate the structure identified in the EFA. The multivariate skewness ranged from −0.38 to 0.54, and the multivariate kurtosis ranged from −0.95 to 0.16, indicating that the data set followed a multivariate normal distribution. Table 6 provides the results of the goodness‐of‐fit indices. The key fit indices demonstrated acceptable model fit, including χ²/df = 2.09, RMSEA = 0.069, TLI = 0.93, and CFI = 0.94 [36, 40]. The factor loadings for each item and their associated error ratios are shown in Figure 3. Convergent validity was confirmed through the calculation of CR and the AVE. The CR values ranged from 0.88 to 0.96, and the AVE values were between 0.58 and 0.69 (Table 7), satisfying the criteria for convergent validity.

Table 6.

Model fit (n = 200).

Goodness of Fit	χ2(p)	χ2/df	RMR	RMSEA	GFI	NFI	TLI	CFI
Baseline	p > 0.05	≤ 3	≤ 0.05	≤ 0.10	≥ 0.90	≥ 0.80	≥ 0.80	≥ 0.80
Results	158.12	2.09	0.04	0. 069	0.84	0.897	0.933	0.943

Abbreviations: CFI, comparative fit index; GFI, goodness‐of‐fit index; NFI, normed fit index; RMR, root mean square residual; RMSEA, root mean square error of approximation; TLI, Tucker–Lewis index.

Figure 3.

Confirmatory factor analysis.

Table 7.

Confirmatory factor analysis results (n = 200).

Factor	Item	Standardized Factor Loading	Critical Ratio	p	CR	AVE	Cronbach's Alpha
Access and obtain mental health information	1	0.75	.62	< 0.001	0.96	0.69	0.737
	2	0.90	8.56	< 0.001
	3	0.87	8.53	< 0.001
	4	0.86	8.43	< 0.001
Understanding mental health information	5	0.81	9.57	< 0.001	0.88	0.62	0.829
	6	0.79	7.64	< 0.001
	7	0.81	8.99	< 0.001
	8	0.83	8.46	< 0.001
	9	0.83	8.5	< 0.001
	10	0.90	8.99	< 0.001
Ability to maintain mental health	11	0.83	8.25	< 0.001	0.92	0.58	0.866
	12	0.83	8.29	< 0.001
	13	0.79	8.16	< 0.001
	14	0.84	9.25	< 0.001
	15	0.84	9.48	< 0.001
	16	0.88	8.95	< 0.001
	17	0.88	8.65	< 0.001
	18	0.89	9.48	< 0.001
	19	0.91	9.75	< 0.001
Adapting to the challenges of women's lives	20	0.91	9.87	< 0.001	0.98	0.63	0.707
	21	0.87	7.38	< 0.001
	22	0.90	9.14	< 0.001
	23	0.86	8.81	< 0.001
	24	0.91	8.74	< 0.001
	25	0.89	8.35	< 0.001
	26	0.91	8.39	< 0.001
	27	0.90	8.27	< 0.001
	28	0.90	9.34	< 0.001
	29	0.93	7.58	< 0.001
	30	0.92	8.27	< 0.001

Abbreviations: AVE, average variance extracted; CR, construct reliability.

3.2.4. Reliability

Reliability was measured via Cronbach's alpha to assess internal consistency. The results revealed that all the factors exhibited robust internal consistency, with Cronbach's alpha values surpassing 0.7 for each subscale and reaching 0.88 for the overall scale (Table 8). Further analysis revealed that the alpha coefficient could not be improved by removing any additional items, confirming the stability of the final scale. These results demonstrate that WoRA‐MHL is a reliable instrument for assessing WoRA‐MHL.

Table 8.

Intraclass correlation coefficients of WoRA‐MHL by dimension and overall.

Domain	Number of items	Cronbach's alpha	ICC	95% CI	p‐value
Access and obtain mental health information	4	0.737	0.816	0.726, 0.882	p < 0.001
Understanding mental health information	6	0.829	0.873	0.816, 0.917	p < 0.001
Ability to maintain mental health	9	0.866	0.831	0.759, 0.889	p < 0.001
Adapting to the challenges of women's lives	11	0.707	0.982	0.975, 0.988	p < 0.001
The scale	30	0.889	0.966	0.953, 0.977	p < 0.001

3.2.5. Stability

The intraclass correlation coefficient (ICC) was calculated by employing the test‒retest method to evaluate the designed instrument's stability and repeatability. Thirty women of reproductive age completed the instrument twice, with a 2‐week interval between tests. The ICC was determined to be 0.966 (p < 0.001), indicating excellent reliability of the instrument (Table 8). Absolute reliability was also assessed by calculating the SEM. For the entire instrument, the SEM was 4.68. The SEM values for the individual dimensions were as follows: access and obtain mental health information (10.2), understanding mental health information (1.89), ability to maintain mental health (28.2), and adapting to the challenges of women's lives (2.41). All SEM values were within the acceptable range, confirming the instrument's reliability (Table 9).

Table 9.

Standard error of measurement of WoRA‐MHL by dimension and overall.

Domain	Mean (standard deviation)	maximum‐minimum	SEM1	MDC2	MIC3	Agreement
Access and obtain mental health information	17.01 (2.84)	13–20	2.10	5.83	1.62	Affirmative
Understanding mental health information	25.36 (3.52)	21–28	1.89	5.25	1.29	Affirmative
Ability to maintain mental health	38.44 (4.33)	29–41	2.28	6.33	1.54	Affirmative
Adapting to the challenges of women's lives	42.07 (6.92)	35–49	2.41	6.70	1.39	Affirmative
The scale	119.7 (26.10)	78–132	4.68	12.96	2.47	Affirmative

¹ Standard error of measurement.

² Minimal detectable change.

³ Minimal important change.

3.2.6. Feasibility

The feasibility of WoRA‐MHL was assessed on the basis of two criteria: (A) the percentage of missing responses and (B) the average time required to complete the instrument. The average completion time was approximately 10 min, and the rate of missing responses ranged from 0% to 1%, indicating the instrument's practicality and ease of use.

3.2.7. Ceiling and Floor Effects

The maximum and minimum scores for each instrument dimension were calculated to assess ceiling and floor effects and the frequency with which respondents achieved these extreme values. The results demonstrated no ceiling or floor effects in any dimension or for the entire instrument (Table 10). This finding indicates that the instrument effectively distinguishes between varying levels of MHL without clustering scores at the extremes.

Table 10.

The ceiling and floor effects for the domains.

Domain	Frequency of minimum score	Floor effect (percentage)	Frequency of maximum scores	Ceiling effect (percentage)
Access and obtain mental health information	0	0	17	8.5
Understanding mental health information	2	1	5	2.5
Ability to maintain mental health	3	1.5	4	2
Adapting to the challenges of women's lives	7	3.5	1	0.5
The scale (30 items)	0	0	0	0

3.2.8. MHL

The mean MHL score for the investigation sample was 79.8 (SD = 17.4). Overall, 71.5% of the respondents demonstrated adequate MHL, whereas 28.5% exhibited limited MHL. No significant differences in MHL were observed across groups differing by age, occupation, marital status, number of children, or history of mental health disorders. However, MHL scores were significantly associated with educational status, with higher education levels correlating with higher MHL scores. These results are summarized in Table 11.

Table 11.

Mental health literacy based on demographic variables.

	Mental health literacy score Mean (SD)	Limited mental health literacy No %	Adequate mental health literacy No %	p
Age				0.097
15–19	74 (12.6)	14 (46.6)	16 (53.3)
20–24	79.8 (15.3)	13 (22.1)	46 (77.9)
25–29	78.1 (11.8)	6 (17.2)	29 (82.8)
30–34	79.7 (12.2)	7 (17.1)	18 (82.9)
35–39	86 (12.1)	3 (13.1)	20 (86.9)
40–44	88 (11.6)	8 (44.4)	10 (55.6)
45–49	72.5 (10.6)	6 (60)	4 (40)
Job				0.061
Employed	98 (14.7)	12 (15.1)	66 (84.9)
Housewife	78.4 (13.9)	45 (36.9)	77 (63.1)
Education				0.047
Primary education	69.6 (12.4)	5 (62.5)	3 (37.5)
Secondary education	80.2 (13.8)	20 (48.7)	21 (52.5)
College education	80.2 (15.9)	32 (21.1)	119 (78.8)
Marriage				0.449
Married	80.08 (12.8)	20 (21.5)	73 (78.5)
Single	78.4 (13.2)	36 (34.9)	67 (65.1)
Divorced/widow	91.8 (11.2)	1 (25)	3 (75)
Number of children				0.064
0	78.4 (13.4)	34 (28.6)	85 (71.4)
1	81 (12.2)	7 (19.4)	29 (80.6)
2	84.4 (11.9)	9 (36)	16 (64)
3	81.6 (11.5)	6 (35.3)	11 (64.7)
4	69.6 (13.1)	1 (33.3)	2 (66.7)
History of mental illness				0.052
Yes	81.4 (13.6)	2 (11.8)	15 (88.2)
No	79.6 (14.3)	55 (30.1)	128 (69.9)
Total	79.8 (17.4)	57 (28.5)	143 (71.5)

4. Discussion

This investigation sought to develop and examine the psychometric properties of WoRA‐MHL by employing an exploratory, sequential, mixed‐methods design. The WoRA‐MHL represents the first instrument specifically developed to assess WoRA‐MHL. The final scale comprises 30 items grouped into four distinct factors: “access and obtain mental health information,” “understanding mental health information,” “ability to maintain mental health,” and “adapting to the challenges of women's lives.” These factors collectively explained 54.42% of the total variance, with individual contributions of 10.01%, 12.43%, 15.89%, and 16.08%, respectively. This instrument's development and psychometric evaluation adhered to the COSMIN checklist, a widely accepted standard for assessing the quality of health status measurement instruments [44].

The internal consistency analysis supports the four‐factor model of WoRA‐MHL, indicating robust reliability. After refinement, the instrument demonstrated excellent internal consistency for the overall scale (α = 0.889) [46]. The internal consistency values for individual factors ranged from good to very good, with alpha values of 0.737, 0.829, 0.866, and 0.707 for factors 1 through 4, respectively. Nevertheless, future studies should extend validity testing to include additional dimensions such as crosscultural and concurrent validity. These efforts should incorporate validated instruments that assess MHL in women to evaluate how well WoRA‐MHL aligns with existing constructs, either as a standalone tool or in combination with others.

Grammatical redundancy can enhance reliability but may limit domain sampling, which is an essential early step in constructing a scale and one of the two key components of content validity. Without proper attention to these factors, a scale may fail to effectively capture the intended construct. In this paper, we utilized a process called ConGRe to quantify, identify, and reduce grammatical redundancy while maintaining conceptual redundancy. This process was carried out during the qualitative content analysis phase by a panel of experts. It included indicators from the linguistic literature that guided decisions made during item writing and before data collection [47].

To ensure the strength and validity of the instrument in measuring the desired constructs, a pilot test was conducted before a large‐scale validation. First, a discussion with colleagues was held to improve the instrument, and an item analysis was performed on a pilot sample to refine the questionnaire before assessing construct validity. During this item analysis, one item was removed based on participant feedback, as its modified Item‐Total Correlation (ITC) was less than 0.3. Additionally, language corrections and item modifications were made based on suggestions from both colleagues and participants. During the validation and item reduction stages, items were considered for deletion when their meanings and implications were already reflected in other items. Deleting these items would not negatively impact the instrument.

MHL is pivotal for improving mental health outcomes and reducing treatment inequities. It equips individuals with the knowledge and skills to recognize symptoms, identify resources, and access appropriate healthcare support [15]. Moreover, it provides healthcare professionals with valuable insights into their patients' educational needs, facilitating the delivery of targeted interventions. Ensuring equitable access to mental health education and care remains a significant challenge for healthcare experts and policymakers, particularly in addressing disparities among underserved populations. The WoRA‐MHL was meticulously designed to meet the specific needs of women of reproductive age, utilizing simple and accessible language to ensure usability. The expert panel's thorough content validity and semantic clarity evaluation significantly enhanced the instrument's relevance and precision. This tool identifies women with inadequate MHL who may benefit from targeted education and support. Notably, existing MHL instruments for women have focused primarily on specific conditions such as depression, or particular life stages, such as the perinatal or adolescent periods [12, 16, 23, 48, 49, 50]. The WoRA‐MHL addresses this gap by offering a comprehensive assessment tailored to the broader MHL needs of reproductive‐aged women.

The first theme of WoRA‐MHL, “access and obtain mental health information” comprises four items and explains 10.01% of the total variance. This dimension demonstrated an ICC of 0.81 and a Cronbach's alpha coefficient of 0.73, confirming its reliability. This construct reflects a woman's ability to independently search for and access mental health information through diverse sources such as virtual networks, therapists, radio and television programs, and mental health applications on mobile phones. It also emphasizes the ability to access educational materials without requiring extensive time or assistance. Additionally, this theme includes the capacity to identify abnormal mental health behaviors, such as depression and anxiety. With the rise of electronic sources for health‐related information, e‐health literacy has become essential. A systematic review showed that e‐health literacy enhances individuals' knowledge and coping skills, which in turn boosts resilience and overall well‐being. This support for mental health ultimately leads to an improved quality of life (QoL). Furthermore, e‐health literacy can help reduce mental health issues among patients, healthcare professionals, and the general public [51].

The MHLS is one of the established instruments for measuring MHL. A 2022 meta‐analysis evaluating the psychometric and methodological quality of the MHLS concluded that it had excellent internal consistency and ranked as the most psychometrically robust instrument among those reviewed [27]. The MHLS is composed of 35 items divided into the following domains: eight items evaluate the ability to recognize mental disorders, four items focus on knowledge and attitudes regarding information sources, two items assess knowledge of risk factors and causes, two items measure knowledge of self‐treatment strategies, three items address knowledge of available professional assistance, and sixteen items examine attitudes that encourage appropriate help‐seeking behaviors or thought processes [23]. In the WoRA‐MHL, we assess all dimensions of the Mental Health Literacy Scale (MHLS) with a focus on women's lives. The fourth dimension, “adapting to the challenges of women's lives,” specifically examines mental health literacy in relation to women's family and social roles, as well as mental health conditions associated with fertility and hormonal changes. This emphasizes the unique aspects of the tool, making it especially suitable for women of reproductive age, given their psychological characteristics and specific needs.

The primary distinction between the MHLS and WoRA‐MHL is the target population. While the MHLS was designed for individuals aged 18 and older, including men and women, its secondary focus was on university students. In contrast, WoRA‐MHL specifically addresses the unique needs of women of reproductive age who face distinct mental health challenges related to menstruation, pregnancy, childbirth, postpartum, and menopause. Unlike the MHLS, which primarily evaluates general mental health knowledge, WoRA‐MHL encompasses the broader and specific dimensions of MHL that are critical for reproductive‐aged women.

The Mental Health Knowledge Questionnaire (MHKQ) consists of 20 items: items 1 through 16 evaluate general knowledge about mental health issues, while items 17 to 20 gauge participants' awareness of four mental health promotion days [52]. Additionally, the Mental Health Attitudes and Knowledge Scale (MAKS) is comprised of 12 items. The first six items address stigma‐related issues across various areas of mental health literacy, including help‐seeking, the ability to give advice, support, employment, treatment, and recovery. The final six items focus on knowledge of mental illness diagnoses [53].

The WoRA‐MHL tool includes 30 items grouped into four distinct factors: “access and obtain mental health information,” “understanding mental health information,” “ability to maintain mental health,” and “adapting to the challenges of women's lives.” Beyond general knowledge about mental health issues, help‐seeking, the ability to give advice, support, employment, treatment, recovery, and knowledge of mental illness diagnoses, this tool also incorporates mental health literacy specific to women of reproductive age. This includes considerations of gender roles, fertility, and hormonal changes.

The second theme of WoRA‐MHL, “understanding mental health information,” accounted for 12.43% of the explained variance and demonstrated strong reliability, with an ICC of 0.87 and a Cronbach's alpha coefficient of 0.82. The findings highlight the necessity for women of reproductive age to develop a solid understanding of mental health terms and information. This knowledge is essential for recognizing, applying, and evaluating mental health resources. It empowers women to improve their mood and relationships while identifying mood disorders caused by hormonal fluctuations, such as those related to menstruation, pregnancy, and the postpartum period. Furthermore, women need to reach a level of insight where they can critically assess the accuracy and validity of mental health information before applying it in their lives.

Research has shown that concepts related to self‐management, self‐care, and self‐help, along with their corresponding strategies, have a beneficial impact on individuals' mental well‐being [54]. Many mothers often feel guilt because they think they have not met the expectations of motherhood. To handle this guilt, mothers frequently use emotional management strategies that are influenced by their gender. These strategies enable them to actively confront their emotions and achieve better psychological outcomes [55]. Maternal experiences regarding risk management during pregnancy can be categorized into nine subscales divided across three main areas. The first area is emotional excitement, which includes feelings of anxiety and despair, as well as joy stemming from hope and optimism, fleeting impulses, and sensations of stagnation and powerlessness. The second area is self‐reflection, which involves actively analyzing ways to mitigate risks, cognitive denial, and a lack of awareness of potential risks. The third area pertains to interventions, which include “problem‐focused and rational” strategies, in addition to “avoidance and ineffective engagement.” Pregnant women with high‐risk pregnancies go through a diverse spectrum of both positive and negative emotions, productive and unproductive thoughts, and actions. These experiences can ultimately contribute to enhanced mental health and support for interventions like “problem‐focused and rational” and “avoidance and ineffective engagement.” The risk management experiences of high‐risk pregnant women encompass a variety of positive and negative emotions, effective and ineffective thoughts and actions, which can contribute to better mental health [56].

This theme aligns closely with Jorm's definition of MHL, which emphasizes “knowledge and beliefs about mental disorders that help individuals recognize, control, or prevent mental health disorders” [15]. Individuals with high MHL are better equipped to identify mental health conditions in themselves or others and seek help from appropriate resources. Increased literacy enables earlier recognition of mental illnesses, which increases the likelihood of effective interventions and social support, ultimately improving mental health outcomes [15].

The third theme, “ability to maintain mental health,” explained 15.89% of the variance and was confirmed with a Cronbach's alpha coefficient of 0.70 and an ICC of 0.83. This dimension underscores the importance of reproductive‐age women understanding the boundaries of normal mood fluctuations associated with hormonal changes during menstruation, pregnancy, and the postpartum period. Women should also be able to recognize abnormal emotional states and take proactive steps to address them. This topic connects with the dimensions of self‐treatment knowledge and the awareness of professional assistance detailed in the MHLS, as well as the treatment and recovery aspects presented in MASK, which are backed by epidemiological data [23, 53]. Furthermore, societal stigma related to mental health challenges and the concern about revealing them can discourage individuals from pursuing help. Such stigma often gives rise to negative gossip that deters social interaction, which is essential for alleviating mental illness symptoms. Indeed, there exists a positive link between mental health literacy and the intent to seek assistance for mental health issues, particularly influenced by critical thinking, which can significantly benefit a person's mental well‐being [57]. Additionally, the societal stigma surrounding mental health and the anxiety about disclosing mental health struggles create negative rumors that impede help‐seeking behavior, while social engagement may help mitigate the symptoms of mental illness [58]. In truth, there's a relationship between mental health literacy and the attitudes towards seeking support for mental health needs [59].

This ability involves creating personal harmony and managing emotions and behaviors, including anger, stress, and anxiety. Women must also be equipped to identify and address general mood disorders such as depression and anxiety, as well as respond appropriately to mental health emergencies. A vital aspect of this theme is reducing the stigma associated with mental health issues, enabling women to seek help without fear of judgment or social stigma. The fourth theme of WoRA‐MHL, “adaptation to the challenges of women's lives,” explained 16.08% of the variance, with a Cronbach's alpha coefficient of 0.88 and an ICC of 0.98, demonstrating excellent reliability. This theme emphasizes the ability of women of reproductive age with adequate MHL to navigate psychological challenges specific to their life stages. These challenges may include adjusting to new roles, such as becoming a wife or mother and managing mental health issues associated with reproductive health, such as postpartum difficulties, emotional distress following abortion, menstrual mood swings, and pregnancy‐related depression.

Our analysis revealed no significant differences in mental health literacy (MHL) across groups based on age, occupation, marital status, number of children, or history of mental health disorders. However, we found that MHL scores were significantly associated with educational status; higher levels of education correlated with higher MHL scores. To establish definitive conclusions regarding the relationship between mental health literacy and demographic characteristics, further research is needed, particularly involving diverse ethnic and racial groups, as well as distinguishing between urban and rural populations with a larger sample size.

The fourth theme, “adapting to the challenges of women's lives,” has been added to the dimensions of previous studies specifically to address the mental health literacy of women of reproductive age. Women with sufficient MHL are better equipped to address family, social, or workplace challenges without enduring psychological harm. They are also more likely to engage in social and friendly groups, fostering resilience and emotional well‐being. These women recognize when to seek support from family members and have the insight to determine when professional help is necessary. Furthermore, they adhere to medical advice, follow treatment regimens, and complete prescribed courses of therapy, improving the effectiveness of interventions. The third and fourth themes of WoRA‐MHL illustrate that, beyond assessing knowledge and understanding of mental health information, this tool measures unique dimensions of MHL specific to women of reproductive age. This makes the WoRA‐MHL a comprehensive, practical, and psychometrically robust instrument with acceptable validity and reliability.

Research has demonstrated that viewing the positive aspect of mental health literacy as a multifaceted and dynamic concept can enhance our understanding of the mechanisms that improve mental health and encourage healthy behaviors. The positive component of mental health literacy is recognized as one of its key elements, encompassing overall positive mental well‐being. This concept is characterized by several key attributes: (a) problem‐solving skills and self‐actualization; (b) personal satisfaction; (c) independence; (d) effective communication and interpersonal skills; (e) self‐control; and (f) a prosocial attitude [60].

In a systematic review aimed at identifying definitions and conceptual frameworks of health literacy, researchers developed an integrated model. The results revealed that the core of the model encompasses competencies related to the processes of accessing, understanding, evaluating, and applying health‐related information. According to the “comprehensive” definition, this process involves four key competencies: (1) Access: The ability to search for, find, and obtain health information. (2) Understanding: The ability to comprehend accessible health information. (3) Evaluation: The capacity to interpret, filter, judge, and assess accessible health information. (4) Application: The ability to communicate and use information effectively to make decisions that support health maintenance and improvement [61]. Given that mental health literacy is also a component of health literacy, it can be concluded that the four dimensions identified in this study align with the four dimensions of Sørensen's model.

On the basis of the findings of this investigation, WoRA‐MHL can be defined as the ability to seek, understand, and effectively use mental health information, particularly concerning disorders specific to the reproductive period. This encompasses the capacity to maintain mental health through appropriate management of physiological mood swings associated with hormonal changes during the reproductive phase. Ultimately, MHL enables women to adapt to the psychological challenges they encounter in familial, social, and occupational settings, supporting their overall well‐being.

There are several important connections between mental health and reproductive health. For instance, psychological issues can arise during pregnancy, childbirth, and the postpartum period. Additionally, mental health can be affected by experiences of violence, including sexual violence. Other factors include adverse maternal outcomes such as stillbirths and miscarriages, surgeries involving the reproductive organs, sterilization, premarital pregnancies in adolescents, and conditions such as human immunodeficiency virus (HIV) infection and acquired immunodeficiency syndrome (AIDS), menopause, and infertility [62].

The World Health Organization (WHO) reports that depression is the leading cause of years lived with disability among adults. Symptoms of depression and anxiety, as well as other unspecified psychiatric disorders and psychological distress, are 2 to 3 times more common in women than in men.

There is significant evidence linking stressful life events and reproductive health issues to depression and anxiety disorders. These events and problems are more prevalent in women's lives, with gender inequality contributing to substantial stress for women. This is particularly evident in cases of violence against girls and women, especially by intimate partners. Such violence can manifest as physical, sexual, or emotional abuse and is a widespread issue globally. Data indicates that in some countries, nearly one in four women experiences sexual violence from an intimate partner. Many women also face violence during pregnancy, leading to adverse outcomes for both them and their babies, such as miscarriage, premature labor, and low birth weight. Research has shown a strong connection between psychological distress, depression, anxiety disorders, and various aspects of reproductive health, including sexually transmitted infections, HIV, childbirth and the postpartum period, pregnancy termination, spontaneous pregnancy loss, menopause, infertility, and a range of gynecological injuries and conditions. Additionally, mental health problems are more prevalent among women who are disproportionately affected by risk factors and negative life experiences that impact their reproductive health [63]. This insight is helping to dismantle long‐standing barriers to health and is beginning to provide new hope for care and cure [64, 65].

This tool can be used to assess the needs and measure the level of mental health literacy among women of reproductive age, both before and after educational, counseling, or treatment interventions. This assessment can help reveal the effectiveness of these interventions. Additionally, it can be utilized within mental health care and treatment systems to screen for mental health literacy in both healthy women and those with mental health disorders. If inadequate mental health literacy is identified, appropriate interventions and counseling can be implemented. Also based on the results of the present study, policymakers can consider programs aimed at improving the identified dimensions of mental health literacy within the care system for women of reproductive age. By providing mental health literacy counseling services in policymaking for care services, this approach can enhance various aspects of mental health quality, ultimately improving overall quality of life.

5. Strengths and Limitations

The WoRA‐MHL is a specialized tool designed to measure WoRA‐MHL, and is tailored to the unique psychological characteristics of this population. Its development included rigorous testing, which demonstrated strong reliability and validity. One key strength of this investigation is the introduction of a novel instrument specifically addressing the MHL needs of women in this demographic. However, certain limitations should be noted. To further validate the instrument, it is essential to compare its results with those obtained from other recognized tools to establish concurrent or criterion validity. This step was not undertaken in the present investigation, emphasizing the necessity of future research to utilize previously validated instruments for comparative analyses. Furthermore, additional research is recommended to explore the applicability of WoRA‐MHL in English and other languages within various cultural and contextual frameworks, emphasizing outcome‐specific measures. The current study has evaluated the grammatical redundancy of the items, and it is recommended that future research consider whether a shorter version of the scale can be developed without sacrificing reliability.

6. Conclusion

The WoRA‐MHL is a comprehensive and psychometrically sound tool encompassing all dimensions of MHL, including aspects of positive WoRA‐MHL. This study provides a valid and reliable method for assessing MHL in this population, offering valuable insights for targeted interventions and improved mental health outcomes.

Author Contributions

Atefeh Safaralinezhad: conceptualization, investigation, writing – original draft, writing – review and editing, software, project administration. Minoor Lamyian: supervision, validation, methodology, conceptualization, data curation. Fazlollah Ahmadi: supervision, methodology, validation, conceptualization, data curation. Zahra Hosseinkhani: software, formal analysis, validation. Ali Montazeri: data curation, supervision, writing – review and editing, project administration, methodology, validation.

Ethics Statement

The ethics committee of Tarbiat Modares University, Tehran, Iran (IR.MODARES.REC.1401.119) approved the study. The participants were informed about the objectives of the study and voluntary participation, and the confidentiality of the data was ensured. They were also assured that only the principal investigator and the study supervisor would have access to the raw interviews. All participants signed the consent form. We confirm that all methods were carried out in accordance with the Helsinki declaration.

Conflicts of Interest

The authors declare no conflicts of interest.

Transparency Statement

The lead author Minoor Lamyian, Ali Montazeri affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

Data Availability Statement

The data that supports the findings of this study are available in the supporting material of this article.